Articles producció científica> Gestió d'Empreses

Dynamic grouping of vehicle trajectories

  • Dades identificatives

    Identificador: imarina:9283416
    Autors:
    Reyes GLanzarini LEstrebou CBariviera A
    Resum:
    Vehicular traffic volume in large cities has increased in recent years, causing mobility problems; therefore, the analysis of vehicle flow data becomes a relevant research topic. Intelligent Transportation Systems mon-itor and control vehicular movements by collecting GPS trajectories, which provides the geographic lo-cation of vehicles in real time. Thus information is processed using clustering techniques to identify vehicular flow patterns. This work presents a methodology capable of analyzing the vehicular flow in a given area, identifying speed ranges and keeping an interactive map updated that facilitates the identification of pos-sible traffic jam areas. The obtained results on three data sets from the cities of Guayaquil-Ecuador, Rome-Italy and Beijing-China are satisfactory and clearly represent the speed of movement of the vehicles, auto-matically identifying the most representative ranges in real time.
  • Altres:

    Autor segons l'article: Reyes G; Lanzarini L; Estrebou C; Bariviera A
    Departament: Gestió d'Empreses
    Autor/s de la URV: Fernández Bariviera, Aurelio
    Paraules clau: Vehicular trajectories Dynamic clustering Data stream
    Resum: Vehicular traffic volume in large cities has increased in recent years, causing mobility problems; therefore, the analysis of vehicle flow data becomes a relevant research topic. Intelligent Transportation Systems mon-itor and control vehicular movements by collecting GPS trajectories, which provides the geographic lo-cation of vehicles in real time. Thus information is processed using clustering techniques to identify vehicular flow patterns. This work presents a methodology capable of analyzing the vehicular flow in a given area, identifying speed ranges and keeping an interactive map updated that facilitates the identification of pos-sible traffic jam areas. The obtained results on three data sets from the cities of Guayaquil-Ecuador, Rome-Italy and Beijing-China are satisfactory and clearly represent the speed of movement of the vehicles, auto-matically identifying the most representative ranges in real time.
    Àrees temàtiques: Software Hardware and architecture Computer vision and pattern recognition Computer science, artificial intelligence Computer science applications Computer science (miscellaneous) Artificial intelligence
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: aurelio.fernandez@urv.cat
    Identificador de l'autor: 0000-0003-1014-1010
    Data d'alta del registre: 2024-09-07
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Journal Of Computer Science & Technology (Jcs&t). 22 (2): 141-150
    Referència de l'ítem segons les normes APA: Reyes G; Lanzarini L; Estrebou C; Bariviera A (2022). Dynamic grouping of vehicle trajectories. Journal Of Computer Science & Technology (Jcs&t), 22(2), 141-150. DOI: 10.24215/16666038.22.e11
    DOI de l'article: 10.24215/16666038.22.e11
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2022
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Artificial Intelligence,Computer Science (Miscellaneous),Computer Science Applications,Computer Science, Artificial Intelligence,Computer Vision and Pattern Recognition,Hardware and Architecture,Software
    Vehicular trajectories
    Dynamic clustering
    Data stream
    Software
    Hardware and architecture
    Computer vision and pattern recognition
    Computer science, artificial intelligence
    Computer science applications
    Computer science (miscellaneous)
    Artificial intelligence
  • Documents:

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